Make 'get_exp_strategy' effective in sql

.gitignore

@@ -94,3 +94,6 @@ modules.rst
 
 # wandb
 wandb/
+
+# checkpoints
+checkpoints/

docs/sphinx_doc/source/tutorial/example_data_functionalities.md

@@ -244,7 +244,7 @@ You can set more config items for this OP (e.g. notification when annotation is
 
 When you start running with the RFT config, the data module will start the OP `human_preference_annotation_mapper`, and then you can find a new project on the "Projects" page of the label-studio server.
 
-![]("../../assets/data-projects.png")
+![](../../assets/data-projects.png)
 
 You can click and enter into this project, and all the samples that need to be annotated are listed on the page.
 

docs/sphinx_doc/source/tutorial/example_multi_turn.md

@@ -122,5 +122,5 @@ and include them in the init files in `trinity/common/workflows/__init__.py`
 
 Then you are all set! It should be pretty simple😄, and both environments converge.
 
-![]("../../assets/alfworld_reward_curve.png")
-![]("../../assets/webshop_reward_curve.png")
+![](../../assets/alfworld_reward_curve.png)
+![](../../assets/webshop_reward_curve.png)

examples/grpo_gsm8k/gsm8k.yaml

@@ -69,6 +69,7 @@ trainer:
   trainer_config_path: 'examples/grpo_gsm8k/train_gsm8k.yaml'
   sft_warmup_iteration: 0 # Set to integer to enable sft warmup
   eval_interval: 50
+  # get_exp_strategy: 'LFU'
 monitor:
   cache_root_dir: ""
   project: "Trinity-RFT-gsm8k"

pyproject.toml

@@ -93,3 +93,6 @@ exclude = '''
     | dist
 )/
 '''
+
+[tool.isort]
+known_third_party = ["wandb"]

trinity/buffer/reader/sql_reader.py

@@ -49,7 +49,7 @@ def read(self, strategy: Optional[ReadStrategy] = None) -> List:
             sortOrder = (desc(self.table_model_cls.priority), desc(self.table_model_cls.id))
 
         else:
-            raise NotImplementedError("Unsupported strategy by SQLStorage")
+            raise NotImplementedError(f"Unsupported strategy {strategy} by SQLStorage")
 
         exp_list = []
         while len(exp_list) < self.batch_size:

trinity/common/config.py

@@ -181,6 +181,7 @@ class TrainerConfig:
 
     # train algorithm
     algorithm_type: AlgorithmType = AlgorithmType.PPO
+    get_exp_strategy: Optional[str] = None
 
     # warmup config
     sft_warmup_iteration: int = 0

trinity/common/verl_config.py

@@ -244,7 +244,6 @@ class Trainer:
     training_rollout_mode: str = "parallel"
     enable_exp_buffer: bool = True
     steps_per_epoch: int = 1280
-    get_exp_strategy: Optional[str] = None
     sync_freq: int = 0
     sft_warmup_iteration: int = 0
     max_actor_ckpt_to_keep: Optional[int] = None

trinity/explorer/explorer.py

@@ -149,8 +149,10 @@ def get_weight(self, name: str) -> torch.Tensor:
 
     def explore(self) -> None:
         """Explore the entire dataset."""
-        explore_status, _ = self.explore_step()
-        while explore_status:
+        while True:
+            explore_status, _ = self.explore_step()
+            if not explore_status:
+                break
             self.sync_weight()
         self.logger.info("Explorer finished.")
 

trinity/trainer/trainer.py

@@ -13,7 +13,7 @@
 
 from trinity.buffer import get_buffer_reader
 from trinity.common.config import Config
-from trinity.common.constants import AlgorithmType
+from trinity.common.constants import AlgorithmType, ReadStrategy
 from trinity.common.experience import Experiences
 from trinity.utils.log import get_logger
 
@@ -81,24 +81,28 @@ def train_iteration(self, algo_type: AlgorithmType = AlgorithmType.PPO) -> Tuple
                     pad_token_id=self.config.buffer.pad_token_id,  # type: ignore
                 )
             )
-        else:
-            exps = self.train_buffer.read()
-            if algo_type.is_rft():
-                return self.engine.train_rft_iteration(
-                    Experiences.gather_experiences(
-                        exps,
-                        pad_token_id=self.config.buffer.pad_token_id,  # type: ignore
-                    )
+        elif algo_type.is_rft():
+            if self.config.trainer.get_exp_strategy:
+                strategy = ReadStrategy(self.config.trainer.get_exp_strategy)
+            else:
+                strategy = None
+            exps = self.train_buffer.read(strategy=strategy)
+            return self.engine.train_rft_iteration(
+                Experiences.gather_experiences(
+                    exps,
+                    pad_token_id=self.config.buffer.pad_token_id,  # type: ignore
                 )
-            elif algo_type.is_dpo():
-                return self.engine.train_dpo_iteration(
-                    Experiences.gather_dpo_experiences(
-                        exps,
-                        pad_token_id=self.config.buffer.pad_token_id,  # type: ignore
-                    )
+            )
+        elif algo_type.is_dpo():
+            exps = self.train_buffer.read()
+            return self.engine.train_dpo_iteration(
+                Experiences.gather_dpo_experiences(
+                    exps,
+                    pad_token_id=self.config.buffer.pad_token_id,  # type: ignore
                 )
-            else:
-                raise ValueError(f"Unsupported algorithm type: {algo_type}")
+            )
+        else:
+            raise ValueError(f"Unsupported algorithm type: {algo_type}")
 
     def sync_weight(self) -> None:
         """Sync the model weight."""